Visualization

This is where you can find information on how to run the visualization software provided by the Blue Waters project. The main software tools being supported are VisIt, Paraview, and yt. Each package has been demonstrated to be scalable and stable. These pages contain the basic information you need to use the software on the Blue Waters environment. In-depth instructions for use of each package can be found at the respective package websites.

VisIt is a free interactive parallel visualization and graphical analysis tool for viewing scientific data on Unix and PC platforms. Users can quickly generate visualizations from their data, animate them through time, manipulate them, and save the resulting images for presentations. VisIt contains a rich set of visualization features so that you can view your data in a variety of ways. It can be used to visualize scalar and vector fields defined on two- and three-dimensional (2D and 3D) structured and unstructured meshes. VisIt was designed to handle very large data set sizes in the terascale range and yet can also handle small data sets in the kilobyte range.

ParaView is an open-source, multi-platform data analysis and visualization application. ParaView users can quickly build visualizations to analyze their data using qualitative and quantitative techniques. The data exploration can be done interactively in 3D or programmatically using ParaView's batch processing capabilities.

yt was designed to be a completely Free, user-extensible framework for analyzing and visualizing astrophysical data, currently working with several different codes, including the "flagship" codes Enzo, Orion, Nyx and FLASH. It relies on no proprietary software – although it can be and has been extended to interface with proprietary software and libraries – and has been designed from the ground up to enable users to be as immersed in the data as they desire.

IDL is a comercial data analysis package. IDL does not scale as VisIt, ParaView, and yt do. It is provided for use on relatively small data and as an aid to transition to more scalable packages as user data grows.